Lightweight Statistical Model Checking in Nondeterministic Continuous Time (Artifact)
datasetposted on 25.09.2018 by Arnd Hartmanns
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
In our ISoLA 2018 paper titled "Lightweight Statistical Model Checking in Nondeterministic Continuous Time", we describe the challenges and state of the art in applying lightweight scheduler sampling to three continuous-time formalisms: After a review of recent work on exploiting discrete abstractions for probabilistic timed automata, we discuss scheduler sampling for Markov automata and apply it on two case studies. We provide further insights into the tradeoffs between scheduler classes for stochastic automata. Making use of our implementations of lightweight scheduler sampling in the 'modes' statistical model checker within the Modest Toolset, we present extended experiments and new visualisations of the distribution of schedulers. This artifact contains (1) the version of 'modes' and (2) the model files used for our experiments, (3) the raw experimental results, (4) summarising tabular views of those results (from which we derived the tables of results and histograms shown in the paper), and (5) the Linux shell scripts that we used to perform the experiments.